Search Results (375)

The increasing demand for sustainable chemical processes has fostered the development of advanced catalytic systems for biomass valorization. In this work, a series of mono-, bi-, and trimetallic oxides (FeO, FeCuO, FeZnO, and FeCuZnO) were successfully synthesized using MIL-101-based MOFs as sacrificial templates. The obtained materials were thoroughly characterized by N₂ adsorption–desorption, XRD, FTIR, and TEM/STEM-EDX to investigate their structural, morphological, and textural properties. Their catalytic performance was evaluated in the selective oxidation of benzyl alcohol, a lignin-derived platform molecule, into benzaldehyde under microwave irradiation as a sustainable heating strategy. The results demonstrate that MOF-derived oxides exhibit superior activity compared to their parent MOFs, highlighting the beneficial effect of thermal treatment on the exposure of active sites. Among the catalysts, heterometallic oxides showed enhanced performance due to synergistic effects between metals. In particular, FeZnO reached a maximum yield of 62.1% towards benzaldehyde at 150 °C and 30 min, outperforming the monometallic oxide. Recycling tests revealed that FeZnO retained higher overall performance than FeCuO, which suffered from progressive copper leaching. These findings confirm the potential of MOF-derived multimetallic oxides as efficient and reusable heterogeneous catalysts for selective biomass-derived alcohol oxidation. The combination of microwave-assisted processes and the tuneable nature of MOF-derived oxides provides a promising pathway for designing sustainable catalytic systems with industrial relevance. Full article

In the context of urban extreme weather events, the efficacy of the “emergency language” employed by governments and public institutions on social media in effectively reaching and guiding the public in a timely manner necessitates a quantifiable evaluation framework. An indicator system was constructed on the basis of Hovland’s persuasion theory. This system comprised five input characteristics (word count/structural clarity, first/second-person perspective, emotional appeal, evidence and framing, and media format) along with three output indicators (reposts, comments, and likes). A data envelopment analysis (DEA) model that is oriented towards output was employed, with disseminators being categorized into four distinct decision-making units: central mainstream media, other government media, local government media, and other media. It is imperative to note that the outputs were subjected to a process of normalization through the implementation of a scale factor. The data were sourced from the Weibo platform within the specified time window, which was from 10:00 on 24 July 2025, to 12:00 on 19 August 2025, with a sample size of 744. The findings revealed substantial disparities in technical efficiency across different disseminator types. A subset of local government media demonstrated a technical efficiency ≈ 1.00 yet low scale efficiency. Posts exhibiting clear structures, actionable points, and accompanying images or videos achieved higher cross-efficiency scores. It is therefore evident that the proposed DEA model provides a benchmark for maximizing dissemination effectiveness under given information characteristics. It is recommended that posting frequencies be maintained at consistent intervals during periods of heightened activity, that a template structure be adopted in accordance with the “fact–action–assistance channel” model, and that the proportion of rich media content be augmented. Full article

Direct growth of high-quality, Bernal-stacked bilayer graphene (BLG) on dielectric substrates is crucial for electronic and optoelectronic devices, yet it remains hindered by poor film quality, uncontrollable thickness, and high-density grain boundaries. In this work, a facile, catalyst-assisted method to grow high-quality, single-crystalline BLG directly on dielectric substrates (SiO₂/Si, sapphire, and quartz) was demonstrated. A single-crystal monolayer graphene template was first employed as a seed layer to facilitate the homoepitaxial synthesis of single-crystalline BLG directly on insulating substrates. Nanostructure Cu powders were used as the remote catalysis to provide long-lasting catalytic activity during the graphene growth. Transmission electron microscopy confirms the single-crystalline nature of the resulting BLG domains, which validates the superiority of the homoepitaxial growth technique. Raman spectroscopy and electrical measurement results indicate that the quality of the as-grown BLG is comparable to that on metal substrate surfaces. Field-effect transistors fabricated directly on the as-grown BLG/SiO₂/Si showed a room temperature carrier mobility as high as 2297 ± 3 cm² V⁻¹ s⁻¹, which is comparable to BLG grown on Cu and much higher than that reported on in-sulators. Full article

Vitamin D deficiency is highly prevalent in Pakistan, but there is limited data on its genetic aspects. This case–control pilot study aimed to determine the association of rs782153744, rs200183599, rs118204011, and rs28934604 with vitamin D deficiency along rs7041 which has been studied in our population. The DNA of a total of 600 subjects (300 cases and 300 controls) was extracted and genotyped by tetra ARMS PCR, followed by Sanger DNA sequencing of exon 4 of the CYP2R1 and CYP27B1 genes and exon 8 of the GC gene. SNP Stat was employed to analyze the data, while logistic regression was used to calculate the p-values and odds ratios (ORs). The R package version R studio (2025.05.1) Build 513 was used to statistically analyze rs782153744. In silico modeling of wild and mutant CYP2R1 and GC proteins was performed in Swiss-Model, Swiss-Dock, Discovery Studio, and PyMol using 3c6g and IJ78 as templates to perform binding pocket analysis of vitamin D3. The rs782153744 showed a protective association in the additive (OR: 0.15, 95% CI: 0.08–0.27, p-value < 0.001), recessive (OR: 0.19, 95% CI: 0.10–0.33, p-value < 0.001), and dominant (OR: 0.19, CI = 0.10–0.33, p-value < 0.001) models, while GC rs7041 (T > A, T > G) displayed a p-value < 0.0001 across all genetic models. Sanger sequencing yielded insignificant results, and the SNPs rs200183599, rs118204011, and rs28934604 had no significant association with vitamin D deficiency. The molecular pocket analysis of wild and mutant CYP2R1 proteins carrying rs782153744 polymorphisms revealed no changes. GC proteins carrying the rs7041 polymorphism revealed a shift in their 3D and 2D configuration, as well as a change in the amino acid residue of the binding pocket of VD3. The risk-associated rs7041 and protective rs782153744 variants back genetic screening for vitamin D deficiency risk stratification, allowing targeted supplementation in predisposed subjects and assisting in formulating a genotype-specific therapeutic approach. Full article

Developing bifunctional electrocatalysts that simultaneously enable green hydrogen production and water purification is essential for advancing sustainable energy and environmental technologies. In this study, we present Cu@Pd core–shell nanostructures fabricated through template-assisted electrodeposition of Cu, followed by galvanic Pd modification on pyrolytic graphite electrodes (PGEs). The optimised catalyst exhibited superior hydrogen evolution reaction (HER) activity, with an onset potential of 70 mV, a low Tafel slope of 33 mV dec⁻¹ and excellent stability during prolonged HER operation. In addition to hydrogen evolution, Cu@Pd/PGE shows significantly enhanced nitrate reduction reaction (NRR) activity compared to Cu/PGE in both alkaline and neutral conditions. Under ideal conditions, the catalyst achieved 60% nitrate removal with high selectivity towards ammonia and minimal nitrite formation, emphasising its superior performance. This enhanced bifunctionality arises from the synergistic Cu–Pd interface, facilitating efficient nitrate adsorption and selective hydrogenation. Despite their high catalytic activity for both HER and NRR, the Cu@Pd nanostructures could often emerge as a versatile platform for integration into sustainable hydrogen production and an effective denitrification process. Full article

Biomass-derived hard carbon (BHC) has attracted considerable attention as a sustainable and cost-effective anode material for sodium-ion batteries (SIBs), owing to its natural abundance, environmental friendliness, and promising electrochemical performance. This review provides a detailed overview of recent progress in the synthesis, structural design, and performance optimization of BHC materials. It encompasses key fabrication routes, such as high-temperature pyrolysis, hydrothermal pretreatment, chemical and physical activation, heteroatom doping, and templating techniques, that have been employed to control pore architecture, defect density, and interlayer spacing. Among these strategies, activation-assisted pyrolysis and heteroatom doping have shown the most significant improvements in sodium (Na) storage capacity and long-term cycling stability. The review further explores the correlations between microstructure and electrochemical behavior, outlines the main challenges limiting large-scale application, and proposes future research directions toward scalable production and integration of BHC anodes in practical SIB systems. Overall, these advancements highlight the strong potential of BHC as a next-generation anode for grid-level and renewable energy storage technologies. Full article

Molecularly imprinted polymer (MIP) biosensors offer an attractive strategy for selective biomolecule detection, yet imprinting proteins with structural fidelity remains a major challenge. In this work, we present a rationally designed electrochemical biosensor for matrix metal-loproteinase-8 (MMP-8), a key salivary biomarker of periodontal disease. By integrating graphene oxide (GO) with electropolymerized poly(eriochrome black T, EBT) films on screen-printed carbon electrodes, the partially reduced GO interface enhanced electrical conductivity and facilitated the formation of well-defined poly(EBT) films with re-designed polymerization route, while template extraction generated artificial antibody-like sites capable of specific protein binding. The MIP-based electrodes were comprehensively validated through morphological, spectroscopic, and electrochemical analyses, demonstrating stable and selective recognition of MMP-8 against structurally similar interferents. Complementary density functional theory (DFT) modeling revealed energetically favorable interactions between the EBT monomer and catalytic residues of MMP-8, providing molecular-level insights into imprinting specificity. These experimental and computational findings highlight the importance of rational monomer selection and nanomaterial-assisted polymerization in achieving selective protein imprinting. This work presents a systematic approach that integrates electrochemical engineering, nanomaterial interfaces, and computational validation to address long-standing challenges in protein-based MIP biosensors. By bridging molecular design with practical sensing performance, this study advances the translational potential of MIP-based electrochemical biosensors for point-of-care applications. Full article

T cell receptor (TCR) profiling using next-generation sequencing (NGS) enables high-throughput, in-depth analysis of repertoire diversity, offering numerous clinical applications. We developed a DNA-based strategy to analyse the TCRβ-chain using NGS and established reference values for T cell repertoire characteristics in 74 healthy donors, including 44 adults, 20 paediatrics, and 10 cord blood units (CBUs). Additionally, four paediatric patients with combined immunodeficiency (CID) or severe CID (SCID) due to deleterious mutations in recombination activating genes (RAG) were analysed. The developed strategy demonstrated high specificity, reproducibility, and sensitivity, and all functional variable and joining genes were detected with minimal PCR bias. All donors had a Gaussian-like distribution of complementary-determining region 3 length, with lower presence of non-templated nucleotides and higher proportion of non-functional clonotypes in CBUs. Both CBUs and paediatrics showed greater convergence and TCRβ diversity was significantly lower in adults and donors with cytomegalovirus-positive serostatus. Finally, an analysis of paediatric patients with RAG-SCID/CID showed significantly shorter CDR3 region length and lower repertoire diversity compared to healthy paediatrics. In summary, we developed a reliable and feasible TCRβ sequencing strategy for application in the clinical setting, and established reference values that could assist in the diagnosis and monitoring of pathological conditions affecting the T cell repertoire. Full article

Despite intense interest in the catalytic potential of transition metal oxide heterostructures, originating from their large surface area and tunable chemistry, the fabrication of well-defined multicomponent oxide coatings with controlled architectures remains challenging. Here, we demonstrate a simple and effective swelling-assisted sequential infiltration synthesis (SIS) strategy to fabricate hierarchically porous multicomponent metal-oxide electrocatalysts with tunable bimetallic composition. A combination of solution-based infiltration (SBI) of transition metals, iron (Fe), nickel (Ni), and cobalt (Co), into a block copolymer (PS73-b-P4VP28) template, followed by vapor-phase infiltration of alumina using sequential infiltration synthesis (SIS), was employed to synthesize porous, robust, conformal and transparent multicomponent metal-oxide coatings like Fe/AlO_x, Fe+Ni/AlO_x, and Fe+Co/AlO_x. Electrochemical assessments for the oxygen evolution reaction (OER) in a 0.1 M KOH electrolyte demonstrated that the Fe+Ni/AlO_x composite exhibited markedly superior catalytic activity, achieving an impressive onset potential of 1.41 V and a peak current density of 3.29 mA/cm². This superior activity reflects the well-known synergistic effect of alloying transition metals with a trace of Fe, which facilitates OER kinetics. Overall, our approach offers a versatile and scalable path towards the design of stable and efficient catalysts with tunable nanostructures, opening new possibilities for a wide range of electrochemical energy applications. Full article

The integration of AI-powered plush robots in educational and therapeutic settings for children with Autism Spectrum Disorders (ASD) necessitates a robust interdisciplinary methodology to evaluate usability, psychological impact, and therapeutic efficacy. This study proposes and applies a four-phase research framework designed to guide the development and assessment of AI-powered plush robots for social rehabilitation and education. Phase 1 involved semi-structured interviews with 13 ASD specialists to explore robot applications. Phase 2 tested initial usability with typically developing children (N = 10–15) through structured sessions. Phase 3 involved structured interaction sessions with children diagnosed with ASD (N = 6–8) to observe the robot’s potential for rehabilitation, observed by specialists and recorded on video. Finally, Phase 4 synthesized data via multidisciplinary triangulation. Results highlighted the importance of iterative, stakeholder-informed design, with experts emphasizing visual properties (color, texture), psychosocial aspects, and adjustable functions. The study identified key technical and psychological evaluation criteria, including engagement, emotional safety, and developmental alignment with ASD intervention models. Findings underscore the value of qualitative methodologies and phased testing in developing child-centered robotic tools. The research establishes a robust methodological framework and provides preliminary evidence for the potential of AI-powered plush robots to support personalized, ethically grounded interventions for children with ASD, though their therapeutic efficacy requires further longitudinal validation. This methodology bridges engineering innovation with psychological rigor, offering a template for future assistive technology research by prioritizing a rigorous, stakeholder-centered design process. Full article

The widespread contamination of aquatic systems by ciprofloxacin (CIP)—a persistent fluoroquinolone antibiotic—poses severe ecological risks due to its antibacterial resistance induction. Conventional sulfate radical-based advanced oxidation processes (SR-AOPs) suffer from inefficient catalyst synthesis, exemplified by low-yield ZIF-67 precursors (typically <25%). To address this, a nitrogen-doped carbon composite Co₃O₄/N@C was synthesized via ammonia-assisted ligand exchange followed by pyrolysis, using N-doped ZIF-67 as a self-sacrificial template. The ammonia incorporation quadrupled precursor yield compared to ammonia-free methods. This catalyst activated peroxydisulfate (PDS) to degrade 95% CIP within 90 min under the optimized conditions (0.5 g/L catalyst, 2 mmol/L PDS, pH 5), representing a 30% enhancement over non-ammonia analogs. Mechanistic studies identified singlet oxygen (¹O₂) as the dominant reactive species, facilitated by N-doped carbon-mediated electron transfer. This strategy overcomes the scalability barrier of MOF-derived catalysts for practical antibiotic wastewater remediation. Full article

Computer-assisted implant surgery (CAIS) using 3D-printed surgical templates has become a preferred approach for improving implant placement accuracy. Despite its clinical advantages over conventional freehand techniques, CAIS remains limited by the presence of cone beam computed tomography (CBCT) metal artefacts, which compromise the 3D data alignment during implant planning and guide fabrication. This narrative review aims to explore the impact of metal artefacts on the accuracy of 3D-printed surgical implant templates and to evaluate current approaches and modifications in implant planning workflows. This article reviews accuracy studies, case reports and technology research on CAIS from the past 5 years. It summarised the CAIS clinical decision framework and data alignment methods to provide alternatives for guided implant therapy in the future. Studies indicate that metal artefacts can distort anatomical data, leading to potential misalignment in 3D data superimposition during surgical guide designs and fabrication. However, various strategies have shown promise in reducing these distortions. Accurate implant planning and template fabrication are essential to ensure clinical success. Special consideration should be given to artefact management during data acquisition. Modified workflows that account for the presence of metal artefacts can enhance guide precision and improve patient outcomes. Full article

Java 8 brought functional programming to the Java language and library, enabling more expressive and concise code to replace loops by using streams. Despite such advantages, for-loops remain prevalent in current codebases as the transition to the functional paradigm requires a significant shift in the developer mindset. Traditional approaches for assisting refactoring loops into streams check a set of strict preconditions to ensure correct transformation, hence limiting their applicability. Conversely, generative artificial intelligence (AI), particularly ChatGPT, is a promising tool for automating software engineering tasks, including refactoring. While prior studies examined ChatGPT’s assistance in various development contexts, none have specifically investigated its ability to refactor for-loops into streams. This paper addresses such a gap by evaluating ChatGPT’s effectiveness in transforming loops into streams. We analyzed 2132 loops extracted from four open-source GitHub repositories and classified them according to traditional refactoring templates and preconditions. We then tasked ChatGPT with the refactoring of such loops and evaluated the correctness and quality of the generated code. Our findings revealed that ChatGPT could successfully refactor many more loops than traditional approaches, although it struggled with complex control flows and implicit dependencies. This study provides new insights into the strengths and limitations of ChatGPT in loop-to-stream refactoring and outlines potential improvements for future AI-driven refactoring tools. Full article

Graphoepitaxial directed self-assembly (DSA) of block copolymers (BCPs) has emerged as a promising strategy for sub-30 nm line spacing patterning in semiconductor nanofabrication. Among the available BCP systems, polystyrene-block-poly (methyl methacrylate) (PS-b-PMMA) has been extensively utilized due to its well-characterized phase behavior and compatibility with standard lithographic processes. However, achieving a high-fidelity pattern with PS-b-PMMA remains challenging, owing to its limited etch contrast and reliance on UV-assisted degradation for PMMA removal. In this study, we report the synthesis of an acid-cleavable lamellar BCP, PS-N=CH-PMMA, incorporating a dynamic Schiff base (-N=CH-) linkage at the junction. This functional design enables UV-free wet etching, allowing selective removal of PMMA domains using glacial acetic acid. The synthesized copolymers retain the self-assembly characteristics of PS-b-PMMA and form vertically aligned lamellar nanostructures, with domain spacings tunable from 36.1 to 40.2 nm by varying the PMMA block length. When confined within 193i-defined trench templates with a critical dimension (CD) of 55 nm (trench width), these materials produced well-ordered one-space-per-trench patterns with interline spacings tunable from 15 to 25 nm, demonstrating significant line spacing shrinkage relative to the original template CD. SEM and FIB-TEM analyses confirmed that PS-N=CH-PMMA exhibits markedly improved vertical etch profiles and reduced PMMA residue compared to PS-b-PMMA, even without UV exposure. Furthermore, Ohta–Kawasaki simulations revealed that trench sidewall angle critically influences PS distribution and residual morphology. Collectively, this work demonstrates the potential of dynamic covalent chemistry to enhance the wet development fidelity of BCP lithography and offers a thermally compatible, UV-free strategy for sub-30 nm nanopatterning. Full article

Asymmetric nanomotors are a class of self-propelled nanoparticles that exhibit asymmetries in shape, composition, or surface properties. Their unique asymmetry, combined with nanoscale dimensions, endows them with significant potential in environmental and biomedical fields. For instance, glutathione (GSH) induced chemotactic nanomotors can respond to the overexpressed glutathione gradient in the tumor microenvironment to achieve autonomous chemotactic movement, thereby enhancing deep tumor penetration and drug delivery for efficient induction of ferroptosis in cancer cells. Moreover, self-assembled spearhead-like silica nanomotors reduce fluidic resistance owing to their streamlined architecture, enabling ultra-efficient catalytic degradation of lipid substrates via high loading of lipase. This review focuses on three core areas of asymmetric nanomotors: scalable fabrication (covering synthetic methods such as template-assisted synthesis, physical vapor deposition, and Pickering emulsion self-assembly), propulsion mechanisms (chemical/photo/biocatalytic, ultrasound propelled, and multimodal driving), and functional applications (environmental remediation, targeted biomedicine, and microelectronic repair). Representative nanomotors were reviewed through the framework of structure–activity relationship. By systematically analyzing the intrinsic correlations between structural asymmetry, energy conversion efficiency, and ultimate functional efficacy, this framework provides critical guidance for understanding and designing high-performance asymmetric nanomotors. Despite notable progress, the prevailing challenges primarily reside in the biocompatibility limitations of metallic catalysts, insufficient navigation stability within dynamic physiological environments, and the inherent trade-off between propulsion efficiency and biocompatibility. Future efforts will address these issues through interdisciplinary synthesis strategies. Full article